Sometimes, the accuracy measures is not enough to show the real performance of a machine learning method [1]}; particularly when applied to an imbalanced dataset [2]}, [3]}, [4]}. Although our dataset of the deep features for persons identification is balanced; having 14 images for each subject/class, we have the other three datasets, which contain the deep features for gender, age, or smile recognition is not fairly balanced as can be seen from the confusion matrices in Tables REF , REF  and REF  respectively. Therefore, we opt for more metrics, namely, F-Measure, ROC Area, and PRC Area. However, instead of repeating all the previous results, we opt for the results of deep features merged using the mean method, as being the best performer, and we used the PCA preserving \(99\%\)  of the data for the same reason. Table REF  shows the results with these metrics. As can be seen from Table REF , and similar to the previous accuracy results, the high values of F-Measure, ROC Area, and PRC Area indicate the robustness of the extracted PCA features from the deep features obtained by mean-merging FC6 and FC7.
